Expert AI Agentic Lead

MatchPoint Solutions is a fast-growing, young, energetic global IT-Engineering services company with clients across the US . We provide technology solutions to various clients like Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more! More recently, we have expanded to working internationally in Canada, China, Ireland, UK, Brazil, and India . Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.

We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!

Job Title: Expert Agentic AI Engineer

Duration: 6 12-month contract-to-hire

Location: Remote as a Contractor and then Hybrid as an FTE. Oakland, CA, Rancho Cordova, CA, Camp Hill, PA or Alpharetta, GA.

Rate : $80 to $85/hr

Description

Our client is seeking an experienced agentic AI engineer to design, build, and operationalize enterprise AI solutions grounded in trusted organizational knowledge. This role is responsible for translating high-value business opportunities into reliable enterprise-grade agentic AI solutions, including autonomous AI agents, grounded assistants, and tool-using workflows.

The ideal candidate will combine strong software engineering capabilities with practical expertise in large language models, retrieval systems, knowledge graphs, evaluation frameworks, and production AI operations. This individual will work closely with business leaders, AI solution leads, knowledge architects, data engineering teams, security and governance partners, and other AI engineers to deliver scalable solutions that connect enterprise data, content, and business processes through a shared knowledge and semantic foundation.

This is a hands-on role for someone who is comfortable moving from use case discovery and solution design through implementation, testing, deployment, monitoring, and continuous improvement. The successful candidate will place equal emphasis on business value, user adoption, responsible AI, engineering quality, and measurable solution performance.

Key Responsibilities

1. Design, build, and deploy agentic AI solutions, including AI agents, multi-agent workflows, semantic search, grounded conversational assistants, and tool-using AI solutions.
2. Develop reusable agent components, tools, connectors, orchestration patterns, evaluation assets, guardrails, and reference implementations that accelerate the delivery of future enterprise AI solutions.
3. Translate business objectives and operational needs into well-scoped agentic AI solutions, working with business stakeholders and AI solutions leads to define expected outcomes, user experiences, decision boundaries, and measurable success criteria.
4. Integrate AI solutions with enterprise knowledge graphs, ontologies, semantic models, APIs, data services, content repositories, and business systems to ground outputs and agent actions in trusted, governed enterprise information.
5. Select and integrate large language models, agent frameworks, retrieval technologies, knowledge platforms, and vendor solutions, making sound build-versus-buy recommendations based on business requirements, enterprise architecture, security, cost, and operational complexity.
6. Establish evaluation frameworks and automated test suites that measure agent task completion, answer quality, groundedness, relevance, tool selection, action accuracy, latency, cost, safety, and user experience.
7. Define and implement guardrails that constrain agent behavior, tool access, data access, decision authority, and autonomous actions based on the sensitivity and risk of each use case.
8. Apply responsible AI practices throughout solution design and delivery, including privacy, security, access control, transparency, human oversight, auditability, bias mitigation, and appropriate use of enterprise information.
9. Partner with operations teams to implement production monitoring and observability for agentic AI applications, including tracing of prompts, retrieval results, reasoning steps where available, tool calls, outcomes, errors, cost, latency, user feedback, and quality trends.
10. Partner with knowledge architects to evaluate how enterprise ontologies, taxonomies, knowledge graphs, and semantic models perform in real applications and provide feedback that informs ongoing improvements to the enterprise knowledge layer.
11. Partner with data engineering teams to define and deliver the data pipelines, embeddings, indexes, APIs, event streams, and data services required by agentic AI applications.
12. Collaborate with enterprise architecture, cybersecurity, privacy, legal, compliance, and AI governance teams to ensure solutions conform to enterprise standards and regulatory obligations.
13. Mentor engineers and contribute to the broader development of agentic AI engineering capabilities, standards, reusable knowledge, and delivery practices across the organization.
14. Support continuous improvement of the enterprise agentic AI delivery lifecycle, from opportunity assessment and prototyping through production deployment, operational monitoring, and solution retirement.
15. Contribute to agentic AI capability roadmaps, platform planning, reference architectures, and long-term enterprise solution delivery strategies. Qualifications
16. Bachelor's degree in computer science, software engineering, information systems, data science, or a related discipline is required. A master's degree in a related discipline is preferred.
17. 6 to 10 years of experience in software engineering, AI engineering, solution architecture, or related technical roles, including at least 2 years building and deploying large language model applications or agentic AI solutions.
18. Proven experience designing, building, and operating production AI applications, preferably including AI agents, tool-using assistants, semantic search, or knowledge-grounded conversational applications.
19. Hands-on experience with agentic AI or generative AI development frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or equivalent technologies.
20. Demonstrated ability to design evaluation methods for generative and agentic AI systems, including evaluation datasets, automated scoring, human review processes, regression testing, and production feedback loops.
21. Experience implementing AI application guardrails, access controls, monitoring, observability, and audit mechanisms.
22. Strong understanding of responsible AI principles, including privacy, security, explainability, transparency, human oversight, bias mitigation, and risk-based controls.
23. Experience integrating AI applications with enterprise data platforms, content repositories, APIs, data services, and identity and access management capabilities.
24. Experience with enterprise knowledge graphs, ontologies, semantic models, or entity resolution is strongly preferred, including experience querying graph technologies through Cypher, SPARQL, or equivalent query languages.
25. Strong analytical, problem-solving, critical-thinking, and organizational skills.
26. Experience working in a scaled agile or similarly structured enterprise delivery environment is preferred.
27. Experience in healthcare payer operations, regulated industries, or environments requiring strong privacy, security, auditability, and human oversight is preferred.

MatchPoint Solutions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.